Search Results for "learning"
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Showing 14 open source projects for "learning"
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ChemCrow
Chemcrow
ChemCrow is an AI-powered framework designed to assist in chemical research and discovery. It integrates AI models with chemical knowledge bases to provide intelligent recommendations for synthesis planning, reaction prediction, and material discovery. This tool helps automate and accelerate research in computational chemistry and drug development. -
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PoseidonQ - AI/ML Based QSAR Modeling
ML based QSAR Modelling And Translation of Model to Deployable WebApps
- This Software was made with an intention to make QSAR/QSPR development more efficient and reproducible. - Published in ACS, Journal of Chemical Information and Modeling . Link : https://pubs.acs.org/doi/10.1021/acs.jcim.4c02372 - Simple to use and no compromise on essential features necessary to make reliable QSAR models. - From Generating Reliable ML Based QSAR Models to Developing Your Own QSAR WebApp. For any feedback or queries, contact kabeermuzammil614@gmail.com - Available on... -
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AMBIT:Chemical Structure DB&Web Service
Chemical structures database & machine learning with web services API
AMBIT offers a cheminformatics data management for chemical substances, structures and nanomaterials. Flexible structure, similarity and study queries storage, descriptor calculation and predictive models building via REST web services.User interface for creating read across assessment and generating reports, aligned with regulatory and industrial requirements. Command line applications also available. Integration with third party tools and databases. -
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GNNPCSAFT
Smart Thermodynamic Modeling with Graph Neural Networks
The GNNPCSAFT app is an implementation of our project that focuses on using Graph Neural Networks (GNN) to estimate the pure-component parameters of the Equation of State PC-SAFT. We developed this app so the scientific community can access the model's results easily. In this app, the estimated pure-component parameters can be used to calculate thermodynamic properties and compare them with experimental data from the ThermoML Archive. To install the GNNPCSAFT app, download the... -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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GNNPCSAFT Web App
Smart Thermodynamic Modeling with Graph Neural Networks
The GNNPCSAFT Web App is an implementation of our project that focuses on using Graph Neural Networks (GNN) to estimate the pure-component parameters of the Equation of State PC-SAFT. We developed this app so the scientific community can access the model's results easily. In this app, the estimated pure-component parameters can be used to calculate thermodynamic properties and compare them with experimental data from the ThermoML Archive. More info on github repository. -
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PyNanoLab
data analysis and Visualization with matplotlib
PyNanoLab contains a variety of tools to complete the data analysis, statistics, curve fitting, and basic machine learning application. Visualization in pynanolab is based on matplotlib. The setup tools is desinged to control and set-up all the details of the figure with a GUI. -
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CRP - Chemical Reaction Prediction
Predicting Organic Reactions using Neural Networks.
The intend is to solve the forward-reaction prediction problem, where the reactants are known and the interest is in generating the reaction products using Deep learning. This Graphical User Interface takes simplified molecular-input line-entry system (SMILES) as an input and generates the product SMILE & molecule. Beam search is used in Version 2, to generate top 5 predictions. Maximum input length for the model is 15 (excluding spaces). -
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RDKit
Open-Source Cheminformatics and Machine Learning
A collection of cheminformatics and machine-learning software written in C++ and Python. NOTE: the RDKit source code and downloads are now in github: https://github.com/rdkit/rdkit The core algorithms and data structures are written in C++. Wrappers are provided to use the toolkit from either Python or Java. Additionally, the RDKit distribution includes a PostgreSQL-based cartridge that allows molecules to be stored in relational database and retrieved via substructure and similarity searches. -
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Fully Managed MySQL, PostgreSQL, and SQL ServerCloud SQL handles your database ops end to end, so you can focus on your app.
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ChemLearing is a lightweight, easy to use e-learning platform - for both the student as well as the teacher.
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3DMolvis is a tool designed to make easy learning of basic structure and function of biological molecules.
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Mathematics, Chemistry and Bio-informatics semantic knowledge editor and simulation environment. It should serve as interactive science learning program or science classroom notebook and workshop.
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Structlab is a machine learning C++ framework for structured domains, which provides a toolbox of learning methods and tools for preprocessing and visualization. It also provides a GUI to setup elaborate experiments in a visual and intuitive way.
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An electronic Periodic Table of the Elements in German language (but translations may follow) made espiacially for pupils in the first years of learning chemistry.
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